Manutenção centrada na confiabilidade com ênfase em colhedoras de cana-de-açúcar: baseado em mapas cognitivos fuzzy

• Ano de defesa: 2023
• Autor(a) principal: Ota Junior, Kazuyochi
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Cornelio Procopio; Brasil; Programa de Pós-Graduação em Engenharia Mecânica; UTFPR
• Palavras-chave em Português: Álcool; Cana-de-açúcar; Álcool como combustível - Indústria; Alcohol; Sugarcane; Alcohol fuel industry; CNPQ::ENGENHARIAS; Engenharia Mecânica
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/32827

The production of ethanol and sugar in sugarcane mills depends directly on the delivery of sugarcane by the CCT sector - Cut Loading and Transport, which, in addition to the culture cultivation sector, is also a sector of prime importance in the process as a whole, formed basically by large agricultural machines such as transhipment tractors, sugarcane harvesters and trucks (road trains). With the arrival of the sugarcane harvester in the field, it provided an even greater production capacity and an increase in harvesting capacity, with the elimination of labor in manual cutting, stimulated by the end of sugarcane burning. However, the processing capacity of industries has also increased, as has demand, requiring more and more mechanical availability of this equipment and greater volume of raw material delivery. The sugarcane harvester is made up of several components that can be analyzed in a way that results in an optimal point for preventive interventions with the help of predictive analyzes and its recurring maintenance. In this context, the need arises to preserve, through maintenance actions, the favorable conditions for the proper functioning of the machines. In this sense, the application of Reality Centered Maintanance (RCM) with quantitative feedback through Fuzzy Cognitive Maps, specifically in sugarcane harvesters, has the potential to increase the reliability of systems. This work explores Reliability Centered Maintenance (RCM) from the perspective of a generic checklist for sugarcane harvester maintenance. Through the execution of corrective actions for failures and defects, it is possible to model a critical and qualitative Fuzzy Cognitive Map (FCM) that will provide an improved quantitative diagnosis. This is presented as a proposal for a computational tool that aims to assist in maintenance management, bringing tangible improvements to the company's systems in a comprehensive way. This aspect gains even more relevance in light of the rise of agricultural technological development. This paper ends with a conclusion and suggestions for future work.